Results 21 to 30 of about 5,130 (182)

Comparison of the Forecast Accuracy of Total Electron Content for Bidirectional and Temporal Convolutional Neural Networks in European Region

Remote Sensing, 2023
Machine learning can play a significant role in bringing new insights in GNSS remote sensing for ionosphere monitoring and modeling to service. In this paper, a set of multilayer architectures of neural networks is proposed and considered, including both
Artem Kharakhashyan, Olga Maltseva
doaj   +1 more source

Gender identification for Egyptian Arabic dialect in twitter using deep learning models

Egyptian Informatics Journal, 2020
Although the number of Arabic language writers in social media is increasing, the research work targeting Author Profiling (AP) is at the initial development phase.
Shereen ElSayed, Mona Farouk
doaj   +1 more source

Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification

Journal of King Saud University: Computer and Information Sciences, 2022
Sentiment analysis has been a well-studied research direction in computational linguistics. Deep neural network models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), yield promising results on text classification ...
Aytuğ Onan
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Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models [PDF]

ITM Web of Conferences
Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression
Wang Yuxin
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A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Machine Learning and Knowledge Extraction, 2021
Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult.
Sourav Malakar, Saptarsi Goswami, Bhaswati Ganguli, Amlan Chakrabarti, Sugata Sen Roy, K. Boopathi, A. G. Rangaraj   +6 more
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LSTM vs. GRU vs. Bidirectional RNN for script generation

CoRR, 2019
Scripts are an important part of any TV series. They narrate movements, actions and expressions of characters. In this paper, a case study is presented on how different sequence to sequence deep learning models perform in the task of generating new conversations between characters as well as new scenarios on the basis of a script (previous ...
Sanidhya Mangal, Poorva Joshi, Rahul Modak   +2 more
openaire   +2 more sources

Visual field prediction using a deep bidirectional gated recurrent unit network model

Scientific Reports, 2023
Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression.
Hwayeong Kim, Jiwoong Lee, Sangwoo Moon, Sangil Kim, Taehyeong Kim, Sang Wook Jin, Jung Lim Kim, Jonghoon Shin, Seung Uk Lee, Geunsoo Jang, Yuanmeng Hu, Jeong Rye Park   +11 more
doaj   +1 more source

Medical Entity Relation Recognition Combining Bidirectional GRU and Attention [PDF]

Jisuanji gongcheng, 2020
Most of existing methods for entity relationship recognition take a single sentence as processing unit,and fail to address tagging errors of entity relationships in the training corpus.Also,they cannot make full use of the mutual reinforcement of ...
ZHANG Zhichang, ZHOU Tong, ZHANG Ruifang, ZHANG Minyu
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A Bidirectional LSTM-RNN and GRU Method to Exon Prediction Using Splice-Site Mapping

Applied Sciences, 2022
Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries ...
Peren Jerfi CANATALAY, Osman Nuri Ucan
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The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

IEEE Access
This work aims to investigate the use of the Recurrent Neural Network (RNN) in automated English grading. In order to achieve this, this work first constructs an automated English grading system based on the Internet of Things (IoT).
Dandan Li, Wenling Li, Yanmei Zhao, Xutao Liu   +3 more
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